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Reflection Separation using a Pair of Unpolarized and Polarized Images
(2020)Advances in Neural Information Processing Systems 32When we take photos through glass windows or doors, the transmitted background scene is often blended with undesirable reflection. Separating two layers apart to enhance the image quality is of vital importance for both human and machine perception. In this paper, we propose to exploit physical constraints from a pair of unpolarized and polarized images to separate reflection and transmission layers. Due to the simplified capturing setup, ...Conference Paper -
OmniSLAM: Omnidirectional Localization and Dense Mapping for Wide-baseline Multi-camera Systems
(2020)2020 IEEE International Conference on Robotics and Automation (ICRA)In this paper, we present an omnidirectional localization and dense mapping system for a wide-baseline multiview stereo setup with ultra-wide field-of-view (FOV) fisheye cameras, which has a 360° coverage of stereo observations of the environment. For more practical and accurate reconstruction, we first introduce improved and light-weighted deep neural networks for the omnidirectional depth estimation, which are faster and more accurate ...Conference Paper -
LIC-Fusion 2.0: LiDAR-Inertial-Camera Odometry with Sliding-Window Plane-Feature Tracking
(2020)2020 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS)Multi-sensor fusion of multi-modal measurements from commodity inertial, visual and LiDAR sensors to provide robust and accurate 6DOF pose estimation holds great potential in robotics and beyond. In this paper, building upon our prior work (i.e., LIC-Fusion), we develop a sliding-window filter based LiDAR-Inertial-Camera odometry with online spatiotemporal calibration (i.e., LIC-Fusion 2.0), which introduces a novel sliding-window ...Conference Paper -
To Learn or Not to Learn: Visual Localization from Essential Matrices
(2020)2020 IEEE International Conference on Robotics and Automation (ICRA)Visual localization is the problem of estimating a camera within a scene and a key technology for autonomous robots. State-of-the-art approaches for accurate visual localization use scene-specific representations, resulting in the overhead of constructing these models when applying the techniques to new scenes. Recently, learned approaches based on relative pose estimation have been proposed, carrying the promise of easily adapting to new ...Conference Paper -
KAPLAN: A 3D Point Descriptor for Shape Completion
(2020)2020 International Conference on 3D Vision (3DV)We present a novel 3D shape completion method that operates directly on unstructured point clouds, thus avoiding resource-intensive data structures like voxel grids. To this end, we introduce KAPLAN, a 3D point descriptor that aggregates local shape information via a series of 2D convolutions. The key idea is to project the points in a local neighborhood onto multiple planes with different orientations. In each of those planes, point ...Conference Paper -
Handcrafted Outlier Detection Revisited
(2020)Lecture Notes in Computer Science ~ Computer Vision – ECCV 2020Local feature matching is a critical part of many computer vision pipelines, including among others Structure-from-Motion, SLAM, and Visual Localization. However, due to limitations in the descriptors, raw matches are often contaminated by a majority of outliers. As a result, outlier detection is a fundamental problem in computer vision and a wide range of approaches, from simple checks based on descriptor similarity to geometric verification, ...Conference Paper -
Infrastructure-Based Multi-camera Calibration Using Radial Projections
(2020)Lecture Notes in Computer Science ~ Computer Vision – ECCV 2020Multi-camera systems are an important sensor platform for intelligent systems such as self-driving cars. Pattern-based calibration techniques can be used to calibrate the intrinsics of the cameras individually. However, extrinsic calibration of systems with little to no visual overlap between the cameras is a challenge. Given the camera intrinsics, infrastructure-based calibration techniques are able to estimate the extrinsics using 3D ...Conference Paper -
Privacy Preserving Structure-from-Motion
(2020)Lecture Notes in Computer Science ~ Computer Vision – ECCV 2020Over the last years, visual localization and mapping solutions have been adopted by an increasing number of mixed reality and robotics systems. The recent trend towards cloud-based localization and mapping systems has raised significant privacy concerns. These are mainly grounded by the fact that these services require users to upload visual data to their servers, which can reveal potentially confidential information, even if only derived ...Conference Paper -
Calibration-Free Structure-from-Motion with Calibrated Radial Trifocal Tensors
(2020)Lecture Notes in Computer Science ~ Computer Vision – ECCV 2020In this paper we consider the problem of Structure-from-Motion from images with unknown intrinsic calibration. Instead of estimating the internal camera parameters through some self-calibration procedure, we propose to use a subset of the reprojection constraints that is invariant to radial displacement. This allows us to recover metric 3D reconstructions without explicitly estimating the cameras’ focal length or radial distortion parameters. ...Conference Paper -
Convolutional Occupancy Networks
(2020)Lecture Notes in Computer Science ~ Computer Vision – ECCV 2020Recently, implicit neural representations have gained popularity for learning-based 3D reconstruction. While demonstrating promising results, most implicit approaches are limited to comparably simple geometry of single objects and do not scale to more complicated or large-scale scenes. The key limiting factor of implicit methods is their simple fully-connected network architecture which does not allow for integrating local information in ...Conference Paper